Home > Research > Publications & Outputs > Scalable Monte Carlo for Bayesian Learning

Research

Links

Keywords

View graph of relations

Scalable Monte Carlo for Bayesian Learning

Research output: Book/Report/Proceedings › Book

Published

Standard

Scalable Monte Carlo for Bayesian Learning. / Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J. et al.
Cambridge: Cambridge University Press, 2025. ( Institute of Mathematical Statistics Monographs).

Research output: Book/Report/Proceedings › Book

Harvard

Fearnhead, P, Nemeth, C, Oates, CJ & Sherlock, C 2025, Scalable Monte Carlo for Bayesian Learning. Institute of Mathematical Statistics Monographs, Cambridge University Press, Cambridge. <https://www.cambridge.org/core/books/scalable-monte-carlo-for-bayesian-learning/7FDC9891DDA086B85AD8AB386F6F3BD3>

APA

Fearnhead, P., Nemeth, C., Oates, C. J., & Sherlock, C. (2025). Scalable Monte Carlo for Bayesian Learning. ( Institute of Mathematical Statistics Monographs). Cambridge University Press. https://www.cambridge.org/core/books/scalable-monte-carlo-for-bayesian-learning/7FDC9891DDA086B85AD8AB386F6F3BD3

Vancouver

Fearnhead P, Nemeth C, Oates CJ, Sherlock C. Scalable Monte Carlo for Bayesian Learning. Cambridge: Cambridge University Press, 2025. ( Institute of Mathematical Statistics Monographs).

Author

Fearnhead, Paul ; Nemeth, Christopher ; Oates, Chris J. et al. / Scalable Monte Carlo for Bayesian Learning. Cambridge : Cambridge University Press, 2025. ( Institute of Mathematical Statistics Monographs).

Bibtex

@book{02b456c344b14af0ba13cb4d5f678c4c,
title = "Scalable Monte Carlo for Bayesian Learning",
abstract = "This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI.",
keywords = "stat.ML, cs.LG, stat.CO, stat.ME",
author = "Paul Fearnhead and Christopher Nemeth and Oates, {Chris J.} and Chris Sherlock",
year = "2025",
month = may,
day = "5",
language = "English",
isbn = "9781009288446",
series = " Institute of Mathematical Statistics Monographs",
publisher = "Cambridge University Press",
address = "United Kingdom",

}

RIS

TY - BOOK

T1 - Scalable Monte Carlo for Bayesian Learning

AU - Fearnhead, Paul

AU - Nemeth, Christopher

AU - Oates, Chris J.

AU - Sherlock, Chris

PY - 2025/5/5

Y1 - 2025/5/5

N2 - This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI.

AB - This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment) have emerged as recently as the last decade, and have driven substantial recent practical and theoretical advances in the field. A particular focus is on methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI.

KW - stat.ML

KW - cs.LG

KW - stat.CO

KW - stat.ME

M3 - Book

SN - 9781009288446

T3 - Institute of Mathematical Statistics Monographs

BT - Scalable Monte Carlo for Bayesian Learning

PB - Cambridge University Press

CY - Cambridge

ER -